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An EM algorithm for linear distortion channel estimation based on observations from a mixture of Gaussian sources

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1 Author(s)
Yunxin Zhao ; Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA

In this work, an expectation maximization (EM) algorithm is derived for maximum likelihood estimation of the autocorrelation function of a linear distortion channel as well as the level of additive noise, under the assumption that the source signal comes from a mixture of Gaussian sources. To facilitate parameter initialization in the EM algorithm, a correlation-matching based estimation algorithm is developed for the channel autocorrelation function. The proposed EM algorithm was evaluated on speech-derived simulated data of multiple autoregressive Gaussian sources and real speech of isolated digits under signal-to-noise ratios (SNRs) of 20 dB down to 0 dB. The algorithm is shown to produce convergent estimation results as well as estimates of signal statistics that lead to significantly improved classification accuracy under additive and convolutive noise conditions

Published in:

Speech and Audio Processing, IEEE Transactions on  (Volume:7 ,  Issue: 4 )

Date of Publication:

Jul 1999

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